library(Seurat)
library(data.table)
library(NMF)
library(rsvd)
library(Rtsne)
library(ggplot2)
library(cowplot)
library(sva)
library(igraph)
library(cccd)
library(KernSmooth)
library(beeswarm)
library(stringr)
library(formatR)
source("../tools.R")
library(DESeq2)
mouse.only.pro <- Load_data(data_dir = "../data/mouse.txt")
rownames(mouse.only.pro) <- unlist(lapply(rownames(mouse.only.pro), str_to_upper))
important.genes <- c("ITGB4", "ABCB5", "KRT19", "ACTB", "KRT12", "KRT5", "GAPDH",
"KRT3", "PAX6", "WNT7A", "KRT14", "TRP63", "KRT10")
mouse.all.pbmc <- DESeq_SeuratObj(X = mouse.only.pro, DESq = FALSE, min.cells = 10,
min.genes = 2)
## [1] "Scaling data matrix"
##
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all.sample.group <- unlist(lapply(mouse.all.pbmc@cell.names, function(x) return(str_split(x,
"_")[[1]][1])))
all.sample.size <- unlist(lapply(mouse.all.pbmc@cell.names, function(x) return(str_split(x,
"_")[[1]][2])))
# reset ident
mouse.all.pbmc <- SetIdent(mouse.all.pbmc, cells.use = mouse.all.pbmc@cell.names,
ident.use = all.sample.size)
mouse.imp.lognorm <- data.frame(FetchData(mouse.all.pbmc, vars.all = important.genes[important.genes %in%
rownames(mouse.all.pbmc@raw.data)]))
library(ggplot2)
library(reshape2)
ITGB4 <- as.numeric(mouse.imp.lognorm[, "ITGB4"])
Positive.idx <- which(ITGB4 > 0)
Negative.idx <- which(ITGB4 == 0)
Positive.data <- mouse.imp.lognorm[Positive.idx, , drop = FALSE]
Negative.data <- mouse.imp.lognorm[Negative.idx, , drop = FALSE]
Positive.data <- Positive.data[, -1] # remove ITGB4
Negative.data <- Negative.data[, -1]
# Positive.t<-data.frame(as.matrix(LogNormalize(Positive.data,display.progress
# = FALSE)))
# Negative.t<-data.frame(as.matrix(LogNormalize(Negative.data,display.progress
# = FALSE))) Positive.t<-data.frame(t(Positive.t[important.genes,]))
# Negative.t<-data.frame(t(Negative.t[important.genes,]))
plot.data <- rbind(Positive.data, Negative.data)
plot.data$condition <- rep(c("ITGB4+", "ITGB4-"), times = c(dim(Positive.data)[1],
dim(Negative.data)[1]))
cell.size <- c(unlist(lapply(rownames(Positive.data), function(x) return(str_split(x,
"_")[[1]][2]))), unlist(lapply(rownames(Negative.data), function(x) return(str_split(x,
"_")[[1]][2]))))
plot.data$cell.size <- cell.size
X <- melt(plot.data)
# p<-ggplot(data = X,aes(y=value,x=condition,fill=cell.size))
# p+geom_violin(trim = FALSE,scale =
# 'width')+facet_wrap(~variable+condition)+
# geom_jitter()+guides(fill=guide_legend(title='Cell Size'))
for (var in as.character(unique(X$variable))) {
p <- ggplot(data = X[X$variable == var, ], aes(y = value, x = condition,
fill = cell.size))
print(p + geom_violin(trim = FALSE, scale = "width") + geom_jitter() + guides(fill = guide_legend(title = "Cell Size")) +
ggtitle(label = var))
}
# p<-ggplot(data = X,aes(y=value,x=condition,fill=cell.size))
# p+geom_boxplot()+guides(fill=guide_legend(title='Cell
# Size'))+facet_wrap(~variable+condition)
for (var in as.character(unique(X$variable))) {
p <- ggplot(data = X[X$variable == var, ], aes(y = value, x = condition,
fill = cell.size))
print(p + geom_boxplot() + guides(fill = guide_legend(title = "Cell Size")) +
ggtitle(label = var))
}
ggplot(data = X, aes(x = value, fill = variable)) + geom_density(kernel = "gaussian") +
scale_x_log10() + facet_wrap(~condition + cell.size)
ggplot(data = X, aes(x = value, fill = variable)) + geom_density(kernel = "gaussian",
position = "stack") + scale_x_log10() + facet_wrap(~condition + cell.size)
ggplot(data = X, aes(x = value, fill = variable)) + geom_histogram() + scale_x_log10() +
facet_wrap(~condition + cell.size)
ITGB4 <- as.integer(mouse.only.pro["ITGB4", ])
Positive.idx <- which(ITGB4 > 0)
Negative.idx <- which(ITGB4 == 0)
Positive.data <- mouse.only.pro[, Positive.idx, drop = FALSE]
Negative.data <- mouse.only.pro[, Negative.idx, drop = FALSE]
Positive.pbmc <- DESeq_SeuratObj(X = Positive.data, min.cells = 10, min.genes = 2)
## [1] "Scaling data matrix"
##
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Positive.sample.group <- unlist(lapply(Positive.pbmc@cell.names, function(x) return(str_split(x,
"_")[[1]][1])))
Positive.sample.cellsize <- unlist(lapply(Positive.pbmc@cell.names, function(x) return(str_split(x,
"_")[[1]][2])))
Positive.pbmc <- SetIdent(Positive.pbmc, cells.use = Positive.pbmc@cell.names,
ident.use = Positive.sample.cellsize)
Negative.pbmc <- DESeq_SeuratObj(X = Negative.data, min.cells = 10, min.genes = 2)
## [1] "Scaling data matrix"
##
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Negative.sample.group <- unlist(lapply(Negative.pbmc@cell.names, function(x) return(str_split(x,
"_")[[1]][1])))
Negative.sample.cellsize <- unlist(lapply(Negative.pbmc@cell.names, function(x) return(str_split(x,
"_")[[1]][2])))
Negative.pbmc <- SetIdent(Negative.pbmc, cells.use = Negative.pbmc@cell.names,
ident.use = Negative.sample.cellsize)
Accordind to the Dispersion vs Avearge expression of Positive and Negative data on ITGB4,they tell us that the although they have similar shape and trend,dispersion of Positive data is more significant than Negative in some genes.
Group_Bar(Positive.pbmc@raw.data, group = Positive.sample.group)
Group_Bar(Positive.pbmc@raw.data, group = Positive.sample.cellsize)
VlnPlot(Positive.pbmc, features.plot = important.genes[important.genes %in%
rownames(Positive.pbmc@raw.data)], y.lab.rot = 90) # Violinn plot of gene ITGB in all sample
Here,do the dimensionality reduction using the PCA, tSNE method
It will take a long time to caculate significant pcs.So,here we use the default value
Positive.pbmc <- PCA.TSNE(object = Positive.pbmc, pcs.compute = FALSE, num.pcs = 28)
FeaturePlot(object = Positive.pbmc, features.plot = important.genes[important.genes %in%
rownames(Positive.pbmc@raw.data)], pt.size = 1, no.legend = FALSE, reduction.use = "pca") # ITGB4 gene in part dataset
FeaturePlot(object = Positive.pbmc, features.plot = important.genes[important.genes %in%
rownames(Positive.pbmc@raw.data)], pt.size = 1, no.legend = FALSE, reduction.use = "tsne") # ITGB4 gene in part dataset
DimPlot(Positive.pbmc, reduction.use = "tsne", pt.size = 4) # grour by sample
DimPlot(Positive.pbmc, reduction.use = "pca", pt.size = 4) # grour by sample
DimHeatmap(Positive.pbmc, reduction.type = "pca", check.plot = FALSE)
The Faetureplot of ITGB4, KRT19, ACTB, KRT12, KRT5, GAPDH, PAX6, KRT14, TRP63, KRT10based on PCA shows that,they only has high expression level in few samples,and expresss lowly in most sample.It means that may be these important genes express differently across sample.The plot also tell us the gene KRT5,GAPDH,PAXX6,KRT14 have more higher expression level than the other important genes.It is consistent with the result of violin plot. But the tSNE and * PCA * plot show that, the sample can not be split apparently.The result may be is not good based on the PCA and tSNE method.
Next,we will have analysis on gene differential expression.Find maker genes across sample.We use the method: **wilcox test**
# Finds markers (differentially expressed genes) for each of the identity
# classes in a dataset
Positive.markers <- FindAllMarkers(Positive.pbmc, test.use = "bimod", print.bar = FALSE)
head(Positive.markers)
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster
## MIR6236 5.919142e-158 3.057346 1.000 0.191 8.022805e-154 6um
## LARS2 4.250212e-152 2.849736 1.000 0.959 5.760737e-148 6um
## RN18S-RS5 4.850037e-146 2.928549 1.000 1.000 6.573740e-142 6um
## GM15564 1.195421e-140 2.251240 1.000 0.856 1.620274e-136 6um
## GM26917 3.212669e-119 3.815874 0.991 0.622 4.354452e-115 6um
## GM23935 8.754999e-112 1.576575 1.000 0.381 1.186653e-107 6um
## gene
## MIR6236 MIR6236
## LARS2 LARS2
## RN18S-RS5 RN18S-RS5
## GM15564 GM15564
## GM26917 GM26917
## GM23935 GM23935
We check whether the important genes are still in the marker genes we found from the DESeq analysis. the genes:ITGB4, KRT19, ACTB, KRT12, KRT5, KRT14 are still in the marker genes.
Positive.heatmap <- Heatmap_fun(genes = important.genes[important.genes %in%
rownames(Positive.pbmc@raw.data)], tpm.data = Positive.pbmc@scale.data,
condition = unique(as.character(Positive.pbmc@ident)), all.condition = as.character(Positive.pbmc@ident))
## There ara 3 conditions
## Whether creat data accurate 0
NMF::aheatmap(Positive.heatmap[[2]], Rowv = NA, Colv = NA, annCol = Positive.heatmap[[1]],
scale = "none")
We have find all marker genes across sample,there are 3438 significant genes(adjust p-value <0.05) in all marker genes.
Positive.pbmc <- KClustDimension(Positive.pbmc, reduction.use = "pca", k.use = 3)
clusters.pca <- Positive.pbmc@meta.data$kdimension.ident
DimPlot(Positive.pbmc, pt.size = 4, group.by = "kdimension.ident")
Positive.pbmc <- KClustDimension(Positive.pbmc, reduction.use = "tsne", k.use = 3)
clusters.tsne <- Positive.pbmc@meta.data$kdimension.ident
DimPlot(Positive.pbmc, pt.size = 4, group.by = "kdimension.ident", reduction.use = "tsne")
Group_Bar(Negative.pbmc@raw.data, group = Negative.sample.group)
Group_Bar(Negative.pbmc@raw.data, group = Negative.sample.cellsize)
VlnPlot(Negative.pbmc, features.plot = important.genes[important.genes %in%
rownames(Negative.pbmc@raw.data)], y.lab.rot = 90) # Violinn plot of gene ITGB in all sample
Here,do the dimensionality reduction using the PCA, tSNE method
It will take a long time to caculate significant pcs.So,here we use the default value
Negative.pbmc <- PCA.TSNE(object = Negative.pbmc, pcs.compute = FALSE, num.pcs = 28)
FeaturePlot(object = Negative.pbmc, features.plot = important.genes[important.genes %in%
rownames(Negative.pbmc@raw.data)], pt.size = 1, no.legend = FALSE, reduction.use = "pca") # ITGB4 gene in part dataset
FeaturePlot(object = Negative.pbmc, features.plot = important.genes[important.genes %in%
rownames(Negative.pbmc@raw.data)], pt.size = 1, no.legend = FALSE, reduction.use = "tsne") # ITGB4 gene in part dataset
DimPlot(Negative.pbmc, reduction.use = "tsne", pt.size = 4) # grour by sample
DimPlot(Negative.pbmc, reduction.use = "pca", pt.size = 4) # grour by sample
DimHeatmap(Negative.pbmc, reduction.type = "pca", check.plot = FALSE)
Next,we will have analysis on gene differential expression.Find maker genes across sample.We use the method: **wilcox test**
# Finds markers (differentially expressed genes) for each of the identity
# classes in a dataset
Negative.markers <- FindAllMarkers(Negative.pbmc, test.use = "bimod", print.bar = FALSE)
head(Negative.markers)
## p_val avg_logFC pct.1 pct.2 p_val_adj cluster
## LARS2 2.749269e-111 4.376151 0.984 0.654 2.968111e-107 6um
## GM23935 3.571136e-108 1.931945 0.781 0.269 3.855398e-104 6um
## GM15564 1.915601e-87 3.928626 0.938 0.602 2.068082e-83 6um
## RN18S-RS5 1.071081e-66 2.924028 1.000 0.919 1.156339e-62 6um
## MIR6236 8.262896e-65 4.354583 0.969 0.110 8.920623e-61 6um
## GM26917 3.809505e-59 3.290449 0.938 0.392 4.112742e-55 6um
## gene
## LARS2 LARS2
## GM23935 GM23935
## GM15564 GM15564
## RN18S-RS5 RN18S-RS5
## MIR6236 MIR6236
## GM26917 GM26917
Negative.heatmap <- Heatmap_fun(genes = important.genes[important.genes %in%
rownames(Negative.pbmc@raw.data)], tpm.data = Negative.pbmc@scale.data,
condition = unique(as.character(Negative.pbmc@ident)), all.condition = as.character(Negative.pbmc@ident))
## There ara 3 conditions
## Whether creat data accurate 0
NMF::aheatmap(Negative.heatmap[[2]], Rowv = NA, Colv = NA, annCol = Negative.heatmap[[1]],
scale = "none")
We have find all marker genes across sample,there are 3765 significant genes(adjust p-value <0.05) in all marker genes.
Negative.pbmc <- KClustDimension(Negative.pbmc, reduction.use = "pca", k.use = 3)
clusters.pca <- Negative.pbmc@meta.data$kdimension.ident
DimPlot(Negative.pbmc, pt.size = 4, group.by = "kdimension.ident")
Negative.pbmc <- KClustDimension(Negative.pbmc, reduction.use = "tsne", k.use = 3)
clusters.tsne <- Negative.pbmc@meta.data$kdimension.ident
DimPlot(Negative.pbmc, pt.size = 4, group.by = "kdimension.ident", reduction.use = "tsne")
When use the DESeq,it must require the gene count matrix satisify that: every gene contains at least one zero, cannot compute log geometric means. So have to take another method to handle data,but I do not know whether it is reasonable.Just try!!!
condition.p <- unlist(lapply(Positive.pbmc@cell.names, function(x) return(str_split(x,
"_")[[1]][2])))
Positive.xdds <- DESeq_CT(count.data = Positive.pbmc@raw.data, condition.1 = condition.p)
plotDispEsts(Positive.xdds, main = "Per-gene Dispersion")
Positive.DESeqGenes <- DESeq_result(Positive.xdds, condition = condition.p)
Positive.DESeqGenes.v <- as.vector(Positive.DESeqGenes)
library(VennDiagram)
grid.draw(venn.diagram(Positive.DESeqGenes.v[1:3], filename = NULL, fill = c("dodgerblue",
"goldenrod1", "darkorange1")))
condition.n <- unlist(lapply(Negative.pbmc@cell.names, function(x) return(str_split(x,
"_")[[1]][2])))
Negative.xdds <- DESeq_CT(count.data = Negative.pbmc@raw.data, condition.1 = condition.n)
plotDispEsts(Negative.xdds, main = "Per-gene Dispersion")
Negative.DESeqGenes <- DESeq_result(Negative.xdds, condition = condition.n)
Negative.DESeqGenes.v <- as.vector(Negative.DESeqGenes)
library(VennDiagram)
grid.draw(venn.diagram(Negative.DESeqGenes.v[1:3], filename = NULL, fill = c("dodgerblue",
"goldenrod1", "darkorange1")))